def train(X, y, epochs=10, batch_size=16):
dataset = IrisDataset(X, y)
num_examples = len(dataset)
loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=True)
print(X.shape[1])
model = IrisNet(input_dim=X.shape[1])
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
criterion = torch.nn.CrossEntropyLoss()
for epoch in range(1, epochs + 1):
num_correct = 0
for i, (inputs, labels) in enumerate(loader):
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
num_correct += (labels == outputs.argmax(1)).sum()
loss.backward()
optimizer.step()
print(
f"Finished: {epoch}, accuracy: {round(num_correct.float().numpy() / num_examples, 4) * 100}%"
)
return model
def run(device):
net = IrisNet()
net.to(device)
optimizer = Adam(net.parameters(), lr=args.lr)
criterion = nn.CrossEntropyLoss()
train_set = IrisDataset(args.dpath)
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True)
losses = []
accuracy = []
# Begin training
for ep in range(args.epochs):
ep_loss = 0
ep_acc = 0
for i, (inputs, labels) in enumerate(train_loader):
inputs = inputs.float().to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs, probs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
ep_loss += loss.item() * len(inputs)
ep_acc += sum(probs.argmax(dim=-1) == labels).item()
losses.append(ep_loss / len(train_set))
accuracy.append(ep_acc / len(train_set))
return net, losses, accuracy